A Nomogram That Includes Neutrophils and High-Density Lipoprotein Cholesterol Can Predict the Prognosis of Acute Ischaemic Stroke

Neutrophil extracellular traps mediated by platelet microvesicles promote thrombosis and brain injury in acute ischemic stroke

AIMS

Neutrophil extracellular traps (NETs) have been implicated in thrombotic diseases. There is no definitive explanation for how NETs form during acute ischemic strokes (AIS). The purpose of our study was to investigate the potential mechanism and role of NETs formation in the AIS process.

METHODS

As well as 45 healthy subjects, 45 patients with AIS had ELISA tests performed to detect NET markers. Expression of high-mobility group box 1 (HMGB1) on platelet microvesicles (PMVs) was analyzed by flow cytometry in healthy subjects and AIS patients' blood samples. We established middle cerebral artery occlusion (MCAO) mice model to elucidate the interaction between PMPs and NETs.

RESULTS

A significant elevation in NET markers was found in patient plasma in AIS patients, and neutrophils generated more NETs from patients' neutrophils. HMGB1 expression was upregulated on PMVs from AIS patients and induced NET formation. NETs enhanced Procoagulant activity (PCA) through tissue factor and via platelet activation. Targeting lactadherin in genetical and in pharmacology could regulate the formation of NETs in MCAO model.

CONCLUSIONS

NETs mediated by PMVs derived HMGB1 exacerbate thrombosis and brain injury in AIS. Video Abstract.

Genetic variation within the pri-let-7f-2 in the X chromosome predicting stroke risk in a Chinese Han population from Liaoning, China: From a case-control study to a new predictive nomogram

Background and objectives

Stroke is the most common cause of disability and the second cause of death worldwide. Therefore, there is a need to identify patients at risk of developing stroke. This case-control study aimed to create and verify a gender-specific genetic signature-based nomogram to facilitate the prediction of ischemic stroke (IS) risk using only easily available clinical variables.

Materials and methods

A total of 1,803 IS patients and 1,456 healthy controls from the Liaoning province in China (Han population) were included which randomly divided into training cohort (70%) and validation cohort (30%) using the sample function in R software. The distribution of the pri-let-7f-2 rs17276588 variant genotype was analyzed. Following genotyping analysis, statistical analysis was used to identify relevant features. The features identified from the multivariate logistic regression, the least absolute shrinkage and selection operator (LASSO) regression, and univariate regression were used to create a multivariate prediction nomogram model. A calibration curve was used to determine the discrimination accuracy of the model in the training and validation cohorts. External validity was also performed.

Results

The genotyping analysis identified the A allele as a potential risk factor for IS in both men and women. The nomogram identified the rs17276588 variant genotype and several clinical parameters, including age, diabetes mellitus, body mass index (BMI), hypertension, history of alcohol use, history of smoking, and hyperlipidemia as risk factors for developing IS. The calibration curves for the male and female models showed good consistency and applicability.

Conclusion

The pri-let-7f-2 rs17276588 variant genotype is highly linked to the incidence of IS in the northern Chinese Han population. The nomogram we devised, which combines genetic fingerprints and clinical data, has a lot of promise for predicting the risk of IS within the Chinese Han population.